Start with the input you have
There is no single best LinkedIn scraper. A recruiter searching for roles, a researcher enriching known URLs, and a sales team comparing company hiring demand need different paid results.
| You have | You need | Start here |
|---|---|---|
| Keywords and locations | A normalized feed of current public job cards | LinkedIn Jobs Search Scraper |
| Job URLs or IDs | Descriptions, criteria, applicant text, and application fields | LinkedIn Job Details Scraper |
| A specialist hiring question | Only rows with direct evidence for a role, contract type, salary, sponsorship, or skill | LinkedIn Contract Jobs Intelligence |
| A list of companies | Comparable role and location evidence by employer | LinkedIn Company Hiring Signals |
| A recurring search | New or changed jobs without rebilling a baseline or unchanged check | Multi-ATS New Jobs Monitor |
Run a bounded sample before editing JSON
A public Apify Task is a saved input you can inspect before starting. These three examples cover the most common first-time paths:
- Search for up to 25 remote AI jobs. Use this when you have a role and location.
- Resolve one current London data-engineering job. Use this when you need a complete public detail record.
- Find contract software jobs with direct contract evidence. Use this when a raw title match is not enough.
Open the Task, inspect its input, and start it only when the query and cap make sense for you. A prepared Task link is not a claim that somebody else has run or paid for it.
Compare the paid unit, not just the sticker price
| Workflow | Current price per 1,000 | One paid unit means |
|---|---|---|
| Jobs Search | $0.34 | One normalized public job card |
| Job Details | $0.70 | One complete public job-detail record |
| Company Hiring Signals | $8 | One supported company-level hiring result |
| Contract Jobs Intelligence | $12 | One job with direct contract-work evidence |
| AI Jobs Demand Intelligence | $18 | One job with direct AI-demand evidence |
Read the live Pricing tab before a large run. Failed requests, diagnostics, duplicates, unsupported classifications, incomplete analyses, monitor baselines, and unchanged checks should not be counted as successful paid intelligence.
Check five things in the first Dataset
- Source identity: every retained row has a stable job ID and canonical public URL.
- Evidence: specialist classifications include the exact public wording that supports them.
- Unknowns: missing salary, seniority, or benefits remain unknown instead of being guessed.
- Diagnostics: the free
RUN_SUMMARYexplains partial requests, invalid inputs, and skipped rows. - Economics: the result count and paid unit match the workflow you intended to buy.
When this collection is the wrong tool
Do not use these Actors for private profiles, employee directories, personal emails, session cookies, login-gated pages, or identity enrichment. Neuton keeps this collection scoped to public job postings because the output is easier to verify, safer to automate, and more stable to maintain.
If you only need a one-off list, start with Jobs Search. If you already know the URLs, use Job Details. Pay for intelligence only when the evidence rule or recurring workflow saves work you would otherwise have to rebuild.
Common first-time questions
Do I need a LinkedIn login?
No. These workflows use public job-posting pages and do not require your LinkedIn account.
Why does intelligence cost more than raw search?
A paid intelligence row must satisfy an evidence rule or complete a bounded analysis. Raw search returns a normalized job card without that decision layer.
What if the sample returns zero rows?
Zero can be a valid result for a narrow query. Check the free run summary, broaden one input at a time, and do not raise every limit at once.
Can I automate the workflow later?
Yes. After the sample is useful, save your own Task and connect it through an Apify schedule, API, webhook, Make, n8n, Zapier, or the Neuton MCP server.
Start with a 25-row public search
Use one role and one location, inspect the source links, and move to detail or intelligence only when the first Dataset proves you need it.
Open the bounded sample